We run our experiments on both unaugmented and augmented data. When augmenting, we add rotations at 90, 180, 270 as extra training classes, similar to [1]}, resulting in 3856 classes.
We use the batch size of 256 and we construct mini-batches such that the contribution of the positive examples in the loss is similar to the contribution of the negative examples. We use the Adam [2]} optimizer with initial learning rate of \(0.0001\)  reduced by factor of \(0.1\)  every 100 epochs, and clip gradients at \(2.5\) . While training, we validate by forming random within-alphabet episodes from the ten alphabets not in the test set. We stop the training once validation accuracy stops improving.
